Genetic variation at the <em>CELF1</em> (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer&#39;s disease and obesity.

Deviations from normal body weight are observed prior to and after the onset of Alzheimer&#39;s disease (AD). Midlife obesity confers increased AD risk in later life, whereas late-life obesity is associated with decreased AD risk. The role of underweight and weight loss for AD risk is controversial. Based on the hypothesis of shared genetic variants for both obesity and AD, we analyzed the variants identified for AD or obesity from genome-wide association meta-analyses of the GERAD (AD, cases = 6,688, controls = 13,685) and GIANT (body mass index [BMI] as measure of obesity, n = 123,865) consortia. Our cross-disorder analysis of genome-wide significant 39 obesity SNPs and 23 AD SNPs in these two large data sets revealed that: (1) The AD SNP rs10838725 (p(AD) = 1.1 x 10(-08)) at the locus CELF1 is also genome-wide significant for obesity (pBMI = 7.35 x 10(-09)). (2) Four additional AD risk SNPs were nominally associated with obesity (rs17125944 at FERMT2, p(BMI) = 4.03 x 10(-05), p(BMI corr) = 2.50 x 10(-03); rs3851179 at PICALM; p(BMI) = 0.002, rs2075650 at TOMM40/APOE, p(BMI) = 0.024, rs3865444 at CD33, p(BMI) = 0.024). (3) SNPs at two of the obesity risk loci (rs4836133 downstream of ZNF608; p(AD) = 0.002 and at rs713586 downstream of RBJ/DNAJC27; p(AD) = 0.018) were nominally associated with AD risk. Additionally, among the SNPs used for confirmation in both studies the AD risk allele of rs1858973, with an AD association just below genome-wide significance (p(AD) = 7.20 x 10(-07)), was also associated with obesity (SNP at IQCK/GPRC5B; p(BMI) = 5.21 x 10(-06); p(corr) = 3.24 x 10(-04)). Our first GWAS based cross-disorder analysis for AD and obesity suggests that rs10838725 at the locus CELF1 might be relevant for both disorders

Across-cohort QC analyses of GWAS summary statistics from complex traits.

Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics Fst statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy

Mendelian randomization study of height and risk of colorectal cancer.

Background: For men and women, taller height is associated with increased risk of all cancers combined. For colorectal cancer (CRC), it is unclear whether the differential association of height by sex is real or is due to confounding or bias inherent in observational studies. We performed a Mendelian randomization study to examine the association between height and CRC risk. Methods: To minimize confounding and bias, we derived a weighted genetic risk score predicting height (using 696 genetic variants associated with height) in 10 226 CRC cases and 10 286 controls. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for associations between height, genetically predicted height and CRC. Results: Using conventional methods, increased height (per 10-cm increment) was associated with increased CRC risk (OR = 1.08, 95% CI = 1.02-1.15). In sex-specific analyses, height was associated with CRC risk for women (OR = 1.15, 95% CI = 1.05-1.26), but not men (OR = 0.98, 95% CI = 0.92-1.05). Consistent with these results, carrying greater numbers of (weighted) height-increasing alleles (per 1-unit increase) was associated with higher CRC risk for women and men combined (OR = 1.07, 95% CI = 1.01-1.14) and for women (OR = 1.09, 95% CI = 1.01-1.19). There was weaker evidence of an association for men (OR = 1.05, 95% CI = 0.96-1.15). Conclusion: We provide evidence for a causal association between height and CRC for women. The CRC-height association for men remains unclear and warrants further investigation in other large studies

Testing the role of predicted gene knockouts in human anthropometric trait variation.

Although the role of complete gene inactivation by two loss-of-function mutations inherited in trans is well-established in recessive Mendelian diseases, we have not yet explored how such gene knockouts (KOs) could influence complex human phenotypes. Here, we developed a statistical framework to test the association between gene KOs and quantitative human traits. Our method is flexible, publicly available, and compatible with common genotype format files (e.g. PLINK and vcf). We characterized gene KOs in 4,498 participants from the NHLBI Exome Sequence Project (ESP) sequenced at high coverage (&gt;100X), 1,976 French Canadians from the Montreal Heart Institute Biobank sequenced at low coverage (5.7X), and &gt;100,000 participants from the GIANT Consortium genotyped on an exome array. We tested associations between gene KOs and three anthropometric traits: body mass index (BMI), height and BMI-adjusted waist-to-hip ratio (WHR). Despite our large sample size and multiple datasets available, we could not detect robust associations between specific gene KOs and quantitative anthropometric traits. Our results highlight several limitations and challenges for future gene KO studies in humans, in particular when there is no prior knowledge on the phenotypes that might be affected by the tested gene KOs. They also suggest that gene KOs identified with current DNA sequencing methodologies probably do not strongly influence normal variation in BMI, height, and WHR in the general human population

Contribution of common non-synonymous variants in <em>PCSK1</em> to body mass index variation and risk of obesity: A systematic review and meta-analysis with evidence from up to 331 175 individuals.

Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m(2)], but their contribution to common obesity (BMI ≥ 30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06-1.24, P = 6.08 × 10(-6)) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04-1.10, P = 3.00 × 10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00-0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00-0.03; P = 5.57 × 10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology